Relevance Indicators for Search Result Sets
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Solution Overview
Problem
Users often construct ineffective search queries that yield unsatisfactory result sets, leading to wasted time reviewing irrelevant documents, as they lack clear indication of relevance until extensive review, and there is a need for intuitive visualization and manipulation of search queries.
Innovation Solution
A method that calculates an estimated relevance score for document result sets and provides visual indicators, along with query visualizations and suggestions for improving search queries, to help users formulate better queries and efficiently identify relevant documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users review documents to determine relevance, then they can identify relevant documents, but they waste significant time reviewing unsatisfactory results
Solution Approach 1:
The system performs preliminary relevance assessment by calculating estimated relevance scores for the entire result set before user review. This preliminary action provides users with advance information about result quality, allowing them to avoid reviewing large numbers of irrelevant documents and focus only on potentially relevant ones.
Solution Approach 2:
The system provides feedback to users through displayed estimated relevance indicators that show the quality of search results. This feedback mechanism allows users to assess result relevance without manual review, enabling them to adjust their search queries based on the feedback and avoid time-consuming review of poor results.
2Ease of operation
If users construct simple search queries, then the search process is quick and easy, but the result sets may be unsatisfactory and not meet search objectives
Solution Approach 1:
The system provides estimated relevance indicators as feedback on search results, allowing users to assess whether their simple queries produced satisfactory results. Users can use this feedback to iteratively refine their queries, improving result reliability while maintaining operational simplicity.
Solution Approach 2:
The system enables users to self-assess result quality through displayed relevance indicators and self-correct their queries accordingly. This self-service approach allows users to maintain simple query construction while achieving reliable results through iterative refinement based on system feedback.
3Loss of information
If users spend extensive time reviewing documents to assess relevance, then they can make informed conclusions, but they lose significant time and productivity
Solution Approach 1:
The system performs preliminary relevance assessment and displays estimated relevance scores before users review documents. This provides users with relevant information in advance, enabling them to make informed decisions about which documents to review without spending extensive time assessing relevance manually.
Solution Approach 2:
The system provides feedback through relevance indicators that inform users about result quality without requiring extensive review. This feedback mechanism maintains information availability while preserving productivity by allowing users to quickly assess whether results meet their needs.
Data Source
AI summary
Systems and methods for displaying estimated relevance indicators for result sets of documents and for displaying query visualizations are disclosed. A method includes receiving a search query including a plurality of query terms. The method further includes searching a database using the search query to identify the result set of documents and calculating an estimated relevance score for the result set of documents. The estimated relevance score is indicative of a degree to which the result set of documents are relevant to the search query. The method further includes providing for display the estimated relevance indicator based on the estimated relevance score. The estimated relevance indicator provides a visual indication of the degree to which the result set of documents are relevant to the search query. Query visualizations including a plurality of nodes and a plurality of connectors are also disclosed.